Identifying rich clubs in spatiotemporal interaction networks

TL;DR

This paper introduces a new metric called WTRC to measure the rich club phenomenon in spatiotemporal networks, revealing significant effects in human mobility data and enhancing understanding of dynamic spatial interactions.

Contribution

The paper develops the WTRC metric and a unified framework to quantify rich club effects in spatially-weighted temporal networks, addressing a gap in existing methods.

Findings

WTRC effectively detects weighted temporal rich club effects in mobility networks.

Unweighted methods often fail or misestimate rich club effects in the same networks.

Spatial and temporal variations of rich clubs are revealed through case studies.

Abstract

Spatial networks are widely used in various fields to represent and analyze interactions or relationships between locations or spatially distributed entities.There is a network science concept known as the 'rich club' phenomenon, which describes the tendency of 'rich' nodes to form densely interconnected sub-networks. Although there are established methods to quantify topological, weighted, and temporal rich clubs individually, there is limited research on measuring the rich club effect in spatially-weighted temporal networks, which could be particularly useful for studying dynamic spatial interaction networks. To address this gap, we introduce the spatially-weighted temporal rich club (WTRC), a metric that quantifies the strength and consistency of connections between rich nodes in a spatiotemporal network. Additionally, we present a unified rich club framework that distinguishes the WTRC effect from other rich club effects, providing a way to measure topological, weighted, and temporal rich club effects together. Through two case studies of human mobility networks at different spatial scales, we demonstrate how the WTRC is able to identify significant weighted temporal rich club effects, whereas the unweighted equivalent in the same network either fails to detect a rich club effect or inaccurately estimates its significance. In each case study, we explore the spatial layout and temporal variations revealed by the WTRC analysis, showcasing its particular value in studying spatiotemporal interaction networks. This research offers new insights into the study of spatiotemporal networks, with critical implications for applications such as transportation, redistricting, and epidemiology.